| Title | Highly Efficient Protection of Biometric Face Samples with Selective JPEG2000 Encryption |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Hofbauer, Heinz, Martínez-Díaz, Yoanna, Kirchgasser, Simon, Méndez-Vázquez, Heydi, Uhl, Andreas |
| Conference Name | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Keywords | biometric encryption, Conferences, Databases, Deep Learning, encoding, face biometrics, face recognition, faces in the wild, JPEG 2000, Metrics, pubcrawl, resilience, Resiliency, Scalability, security evaluation, Selective Encryption, Signal processing, Transform coding |
| Abstract | When biometric databases grow larger, a security breach or leak can affect millions. In order to protect against such a threat, the use of encryption is a natural choice. However, a biometric identification attempt then requires the decryption of a potential huge database, making a traditional approach potentially unfeasible. The use of selective JPEG2000 encryption can reduce the encryption's computational load and enable a secure storage of biometric sample data. In this paper we will show that selective encryption of face biometric samples is secure. We analyze various encoding settings of JPEG2000, selective encryption parameters on the "Labeled Faces in the Wild" database and apply several traditional and deep learning based face recognition methods. |
| DOI | 10.1109/ICASSP39728.2021.9413941 |
| Citation Key | hofbauer_highly_2021 |
Highly Efficient Protection of Biometric Face Samples with Selective JPEG2000 Encryption